This talk will introduce how information is hidden in electroencephlogram (EEG) signals and discuss the resulting challenges for single-trial EEG decoding. Methods for feature extraction and multi-variate classification are presented. Moreover, the talk will explain the basic concepts of Brain-Computer Interfaces (BCIs).

17:15 - 17:35 Uhr "BCIs as a tool for cognitive neuroscience"

As a novel example of BCI technology, I will show how it can be employed to answer questions in a different field of research, here in cognitive neuroscience. There is a strong debate about the fact that spontaneous movements are preceded by predictive EEG signals, in particular as some studies suggest that those signals start prior to the conscious decision to move. We used BCI technology for an investigation of this phenomenon in which real-time prediction of movement decisions is used to intervene in the experimental flow. Our findings suggest that voluntary control over choice-predictive brain signals is limited, but movements can be cancelled up to a point-of-no-return which was found to be on average around 200 ms before EMG onset and movement completion can be avoided even after that. This result has important implications for potential applications of BCI technology and contributes to ongoing disucssions in cognitive neuroscience.

17:35 - 18:00 Uhr"Employing BCIs in the maritime world"

BCI technology can be used to obtain a continuous, time-resolved measure of user states. This approach can be used to assess devices and interfaces. A navigation system for a car, e.g., may be tested and optimized with respect to how little it distracts the driver from the driving task as quantified by BCI-derived measures of workload or focused attention. In this talk, I will show how this approach could be used for aid safety critical decisions about inland waterways. This potential application imposes high requirements on the decoding algorithms. The various challenges are discussed.